Triple
T30625163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tendai |
E779555
|
entity |
| Predicate | influencedSchool |
P181159
|
FINISHED |
| Object | Jōdo-shū |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Jōdo-shū | Statement: [Tendai, influencedSchool, Jōdo-shū]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedSchool Context triple: [Tendai, influencedSchool, Jōdo-shū]
-
A.
influencedInstitution
Indicates that one entity has had a shaping or affecting impact on the development, policies, direction, or character of an institution.
-
B.
schoolOf
Indicates that an educational institution is the one where a person studied, worked, or is otherwise academically affiliated.
-
C.
attributedSchool
Indicates that an entity is associated with or credited to a particular school, institution, or educational affiliation.
-
D.
effectOnSchools
Indicates the impact or influence that something has on schools, such as changes to their conditions, performance, operations, or environment.
-
E.
alsoAttendedSchool
Indicates that two or more entities attended the same school in addition to any other schools they may have attended.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f224a431548190a44ad9d088dbf91f |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7626667f48190ad90867eb67ec582 |
completed | May 3, 2026, 2:57 p.m. |
| PD | Predicate disambiguation | batch_69f76175d6608190b60b268e20f49ed9 |
completed | May 3, 2026, 2:53 p.m. |
| PDg | Predicate description generation | batch_69f762651e088190baa21f25378a6065 |
completed | May 3, 2026, 2:57 p.m. |
Created at: April 29, 2026, 8:27 p.m.